Product & Strategy

Depending on who you ask, SaaS is either finished or is about to have its best decade yet (after the messy part subsides).

Spoiler: both are true, just not for the same companies. We unpack the great divide: which business models, product categories, and pricing structures will survive the AI transition, and which ones are Blockbuster in 2004 not admitting it yet. Honest answers from operators on both sides of the line.

Every founder thinks their product is sticky. Most are wrong.

Five concrete things you can do right now when the threat of being replaced is nipping at your heels: engineering data ownership, system-of-record positioning, deliberate switching costs, outcome lock-in, and the trust contract. Walk out with a working list, not a framework.

Generic SaaS is the most dangerous place to be in 2026.

The AI-native entrants are coming for the broad market and the giants are eating the middle. The founders’ surviving are finding loyalty in vertical: specific industries, specific workflows, specific compliance requirements, distinct datasets a foundation model can’t replicate overnight. Learn how to find your niche, own it completely, and turn “too small to bother with” into your unfair advantage.

Whether you’re unknown, scaling, or defending turf: your buyers are asking the same question: why should I trust you?

In a market flooded with AI tools and AI-generated promises, credibility has become the new go-to-market differentiator. This conversation is about how to build the kind of credibility that actually moves deals, earns bigger bets, and makes you the safe choice in a market where everyone’s not exactly trusting the AI you may be offering.

Every SaaS founder is asking the same question: if AI agents can do the work, who wins?

The answer might have less to do with your product and more to do with your distribution, and whether your agents are making decisions with real context or flying blind. Context is the data, history, and signal your agent needs to act intelligently. Without it, it’s guessing. In an agentic world, the companies that scale will be the ones whose AI has the right information at the right moment. Context isn’t a feature. It’s the foundation.

You’ve deployed agents across your org — congrats!

Now who’s actually in charge of them? “The AI handles it” is not a governance strategy. This session gets into the emerging frameworks around agent identity, permissions, and accountability when the rulebook is still being written. For founders who’d rather design the policy than answer the lawsuit.

Right now there’s a genuine infrastructure shift happening around how AI agents talk to each other and to SaaS products. Model Context Protocol or MCP is the clearest example.

For SaaS founders, the question is whether your product becomes a node in someone else’s agent network or gets bypassed entirely. What that means for your roadmap, your distribution, and where your value actually lives